Using US Department of Justice data on local political corruption, I find that firms in more corrupt areas hold less cash and have greater leverage than firms in less corrupt areas. The results are robust to including a range of controls and to using an instrumental variable approach, two alternative survey measures of corruption, and propensity score matching. Further, the association between corruption and leverage is largest among firms that operate primarily around their headquarters. Overall, the evidence is consistent with the hypothesis that firms manage liquidity downward and debt obligations upward to limit expropriation by corrupt local officials.

Using European stock data from two different venues and time periods for which we can identify each trade's aggressor, we test the performance of the bulk volume classification (Easley et al. (2016); BVC) algorithm. BVC is data efficient, but may identify trade aggressors less accurately than “bulk” versions of traditional trade-level algorithms. BVC-estimated trade flow is the only algorithm related to proxies of informed trading, however. This is because traditional algorithms are designed to find individual trade aggressors, but we find that trade aggressor no longer captures information. Finally, we find that after calibrating BVC to trading characteristics in out-of-sample data, it is better able to detect information and to identify trade aggressors. In the new era of fast trading, sophisticated investors, and smart order execution, BVC appears to be the most versatile algorithm.

We study the New York City taxi market to examine whether an excise tax hike on cigarettes corresponds to smoker taxi drivers more frequently cheating their customers. Increased cheating could be motivated by both financial pressures and as a reaction to unfair treatment (as surveyed smokers view cigarette tax hikes as quite unfair). We examine this question using detailed ride-level data where we can identify a rare but fraudulent overcharging technique (cheating) and a subsample of taxi drivers who smoke (affected taxpayers, identified via tickets for smoking in a cab). In difference-in-differences regressions we find that following a cigarette tax hike, taxi drivers who smoke are approximately 1.5 times more likely to cheat customers than other drivers. Our findings are strongest in the subsample of smokers with consistently low earnings.

The growing popularity of home-sharing platforms such as Airbnb, partly fueled by hosts’ ability to evade local taxes and regulations, has been shown to elevate housing costs by reallocating long-term housing units to the short-term rental market. This study assesses whether enhanced tax enforcement can mitigate this trend. We analyze staggered tax collection agreements between Airbnb and Florida counties, wherein Airbnb collects taxes from the hosts directly. Using a difference-in-differences methodology, we find these agreements significantly slow the growth of housing costs, highlighting the importance of tax policy in addressing the sharing economy’s influence on housing affordability.